Data-Driven Campaigning and Political Parties
Bibliographic record
Abstract
Abstract What is data-driven campaigning? According to prevailing accounts, this idea describes the rise of increasingly sophisticated, highly targeted, and often invasive uses of data. Deployed to suppress votes, manipulate voter preferences, or boost a candidates’ popularity, the power of data is seen to be transforming campaign practice and raising concerns over democratic processes. And yet, there is a significant problem with these ideas: there is at best a partial understanding of the nature of data-driven campaigning, and limited clarity about its implications. This book provides unprecedented insight into the conduct of data-driven campaigns. Presenting data from interviews with over 300 professional campaigners in Australia, Canada, Germany, the United Kingdom, and the United States, the authors provide unique insight into the components of data-driven campaigning by political parties. They make three key contributions. First, distinguishing between data, analytics, technology, and personnel, they provide unmatched descriptive insight into these four components of data-driven campaigning, revealing significant variation in its operationalization depending on party and country context. Second, introducing a novel multi-level theoretical framework, they isolate systemic, regulatory, and party-level variables which help explain the reasons for these differences. Third, they consider the implications of these findings for debates about democracy, data, and technology in the twenty-first century. Cumulatively these contributions reveal data-driven campaigning to come in different forms that are not inherently problematic. Giving voice to practitioner perspectives, through interviews and innovative vignettes, this book recasts the debate around data-driven campaigning, offering important lessons for scholars, campaigners, and policymakers alike.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".